Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F24%3A10254898" target="_blank" >RIV/61989100:27230/24:10254898 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001215756400001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001215756400001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.heliyon.2024.e26665" target="_blank" >10.1016/j.heliyon.2024.e26665</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Popis výsledku v původním jazyce

This research introduces the Multi -Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi -objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition -Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non -dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi -objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real -world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two -bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non -dominated sorting grey wolf optimizer (NSGWO), multiobjective multi -verse optimization (MOMVO), non -dominated sorting genetic algorithm (NSGA-II), decomposition -based multiobjective evolutionary algorithm (MOEA/ D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

Název v anglickém jazyce

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Popis výsledku anglicky

This research introduces the Multi -Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi -objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition -Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non -dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi -objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real -world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two -bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non -dominated sorting grey wolf optimizer (NSGWO), multiobjective multi -verse optimization (MOMVO), non -dominated sorting genetic algorithm (NSGA-II), decomposition -based multiobjective evolutionary algorithm (MOEA/ D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20300 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Heliyon

ISSN

2405-8440

e-ISSN

2405-8440

Svazek periodika

10

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

33

Strana od-do

"nestrákováno"

Kód UT WoS článku

001215756400001

EID výsledku v databázi Scopus

—